A differential privacy mechanism with network effects for crowdsourcing systems

A differential privacy mechanism with network effects for crowdsourcing systems

Authors:

Luo, YJennings, NR

Item Type:

Conference Paper

Abstract:

In crowdsourcing systems, it is important for the crowdsource campaign initiator to incentivize users to share their data to produce results of the desired computational accuracy. This problem becomes especially challenging when users are concerned about the privacy of their data. To overcome this challenge, existing work often aims to provide users with differential privacy guarantees to incentivize privacy-sensitive users to share their data. However, this work neglects the network effect that a user enjoys greater privacy protection when he aligns his participation behaviour with that of other users. To explore the network effect and provide a suitable differential privacy guarantee, we design PINE (Privacy Incentivization with Network Effects). PLNE is a mechanism that maximizes the initiator's payoff while providing participating users with privacy protections.